glinski_metabolites

tested hypotheses

• observed vivo versus in vitro degradation rates are the same, or observed vivo rates are lower than in vitro rates (due to other degradation processes, uptake kinetics?)
• first order kinetics are michaelis-menton, second order
• are in vitro indicative of in vivo

things to do

• michaelis menton first order comparison for both experiments
• non-linear goodness of fit

install and load supporting libraries

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

## 
## 'drc' has been loaded.

## Please cite R and 'drc' if used for a publication,

## for references type 'citation()' and 'citation('drc')'.

## Loading required package: lme4

## Loading required package: Matrix

import experimental exposure and microsomal data

Load csv files with experimental data and microsome data sets. The beolw may return false but still be OK if rstudio does not have privileges to data directory (e.g., attached drive).

##      time      parent     analyte     matrix        conc replicate
## 4      15    atrazine    atrazine microsomes   0.5514354         1
## 47      0    atrazine    atrazine microsomes          NA         1
## 61     90    atrazine    atrazine microsomes          NA         3
## 92      0    atrazine    atrazine microsomes 697.4815010         1
## 95     15    atrazine    atrazine microsomes 161.9606512         1
## 98     30    atrazine    atrazine microsomes 237.9761898         1
## 101    60    atrazine    atrazine microsomes 255.9782700         1
## 104    90    atrazine    atrazine microsomes 121.3271776         1
## 127     0    atrazine    atrazine microsomes          NA         1
## 134    31    atrazine    atrazine microsomes          NA         2
## 169    60    atrazine         dea microsomes   0.3080022         4
## 206    15    atrazine         dea microsomes          NA         1
## 209    30    atrazine         dea microsomes          NA         1
## 212    60    atrazine         dea microsomes          NA         1
## 215    90    atrazine         dea microsomes          NA         1
## 231    90    atrazine         dea microsomes          NA         2
## 235     0    atrazine         dea microsomes          NA         3
## 255    30    atrazine         dea microsomes          NA         2
## 259    60    atrazine         dea microsomes          NA         3
## 344     0    atrazine         dia microsomes   0.0000000         1
## 347    15    atrazine         dia microsomes   0.3204253         1
## 350    30    atrazine         dia microsomes   0.9284476         1
## 353    60    atrazine         dia microsomes   1.5356847         1
## 356    90    atrazine         dia microsomes   1.0590508         1
## 359     0    atrazine         dia microsomes          NA         1
## 365    30    atrazine         dia microsomes          NA         1
## 366    30    atrazine         dia microsomes          NA         2
## 370    60    atrazine         dia microsomes          NA         3
## 372    90    atrazine         dia microsomes          NA         2
## 379    15    atrazine         dia microsomes          NA         3
## 386    90    atrazine         dia microsomes          NA         1
## 478    60    fipronil     fipsulf microsomes   0.0414000         2
## 547    15    fipronil    fipronil microsomes   0.0000000         2
## 591    15    fipronil    fipronil microsomes  55.7278739         1
## 635     0 triadimefon        tdla microsomes   2.3211093         3
## 657    60 triadimefon        tdla microsomes   0.7265999         1
## 668    15 triadimefon        tdla microsomes   3.7310331         3
## 710     0 triadimefon        tdla microsomes   5.2664595         3
## 713    15 triadimefon        tdla microsomes   5.2998384         3
## 803    15 triadimefon        tdlb microsomes   3.3031594         3
## 927    60 triadimefon triadimefon microsomes          NA         1
## 936    15 triadimefon triadimefon microsomes          NA         1
## 1023    0 triadimefon triadimefon microsomes 229.2656431         1
##      microMexp  X
## 4       0.7120 NA
## 47     50.0000 NA
## 61     50.0000 NA
## 92    125.0000 NA
## 95    125.0000 NA
## 98    125.0000 NA
## 101   125.0000 NA
## 104   125.0000 NA
## 127   250.0000 NA
## 134   250.0000 NA
## 169    10.0000 NA
## 206   125.0000 NA
## 209   125.0000 NA
## 212   125.0000 NA
## 215   125.0000 NA
## 231     0.7120 NA
## 235     3.1684 NA
## 255   250.0000 NA
## 259   250.0000 NA
## 344   125.0000 NA
## 347   125.0000 NA
## 350   125.0000 NA
## 353   125.0000 NA
## 356   125.0000 NA
## 359     0.7120 NA
## 365     0.7120 NA
## 366     0.7120 NA
## 370     0.7120 NA
## 372     0.7120 NA
## 379     3.1684 NA
## 386     3.1684 NA
## 478    75.0000 NA
## 547    10.0000 NA
## 591   150.0000 NA
## 635     0.6800 NA
## 657    10.0000 NA
## 668     2.8900 NA
## 710   100.0000 NA
## 713   100.0000 NA
## 803     2.8900 NA
## 927    10.0000 NA
## 936     2.8900 NA
## 1023  250.0000 NA

experimental exposure data structure

Check out structure of imported data sets.

Set time and replicate fields as factors for later statistical inference.

microsome experiment data structure

Check out structure of imported data set for the microsome analysis.

Set time and replicate fields as factors for later statistical inference.

analyze microsomal data

The microsomal experiment has 3-4 replicates for each pesticide. The experiment progressively increases the concentration of the substrate (microMexp) and measures the enzyme velocity. At each substrate concentration the reaction is quenched after a specific period of time (0, 15, 30, 60, 90 mins). This time series data is used to estimate a slope associated with each substrate concentration. Substrate concentration and linear velocity slopes are then fit using Michaelis-Menten kinetics to estimate reaction rates for each pesticide.

non-linear fitting

We use the dose-response model (drm) package from R for the fitting of the rate constant. We provide it the 2-parameter Michaelis-Menton function (mm2) as an argument. It uses the MM.2 function that is in the drc package and therefore is a drc object that contains a list of class drcMean, containing the mean function, the self starter function, the parameter names and other components such as derivatives and a function for calculating ED values. It is not shifted (??) so uses the fit f(x,(c,d,e)) = c + (d-c)/(1+e/x). Where x is the dose, it is a strictly increasing function so we reverse the sign of the slopes for the parents. e is the dose halfway between c and d, for the 2-parameter model c = 0.

## Michaelis-Menten 
## (2 parameters) 
## In 'drc':  MM.2

atrazine

The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.

## [1] "Substrates:"

## [1] "here are the rates"

## 
## Model fitted: Michaelis-Menten (2 parms)
## 
## Parameter estimates:
## 
##                 Estimate Std. Error    t-value p-value
## d:(Intercept) 2.0317e+04 5.6748e+04 3.5802e-01  0.7309
## e:(Intercept) 3.6081e+03 1.0594e+04 3.4059e-01  0.7434
## 
## Residual standard error:
## 
##  109.7349 (7 degrees of freedom)

We are interested in the reaction rate, v, which is a function of available substrate (pesticide) and the presence of an enzyme. We get Vmax from the asymptote and K = 0.5*Vmax. Biochemical reactions with a single substrate are typically assumed to follow Michaelis-Menten kinetics even if they deviate from the basic model assumptions.

We are fitting the 2-parameter model.

Used to estimate slopes.

dea

The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.

## [1] "Substrates:"

## [1] "here are the rates"

## 
## Model fitted: Michaelis-Menten (2 parms)
## 
## Parameter estimates:
## 
##               Estimate Std. Error  t-value p-value
## d:(Intercept) 119.0693    24.4160   4.8767  0.0018
## e:(Intercept)  31.9046    25.8547   1.2340  0.2570
## 
## Residual standard error:
## 
##  20.38128 (7 degrees of freedom)

We are interested in the reaction rate, v, which is a function of available substrate (pesticide) and the presence of an enzyme. We get Vmax from the asymptote and K = 0.5*Vmax. Biochemical reactions with a single substrate are typically assumed to follow Michaelis-Menten kinetics even if they deviate from the basic model assumptions.

We are fitting the 2-parameter model.

Used to estimate slopes.

dia

The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.

## [1] "Substrates:"

## Warning in rlm.default(x, y, weights, method = method, wt.method =
## wt.method, : 'rlm' failed to converge in 20 steps

## [1] "here are the rates"

## 
## Model fitted: Michaelis-Menten (2 parms)
## 
## Parameter estimates:
## 
##                 Estimate Std. Error    t-value p-value
## d:(Intercept) 5.3408e+03 1.6487e+04 3.2394e-01  0.7555
## e:(Intercept) 8.5689e+03 2.7010e+04 3.1724e-01  0.7603
## 
## Residual standard error:
## 
##  16.48602 (7 degrees of freedom)

triadimefon

The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.

## [1] "Substrates:"

## [1] "here are the rates"

## 
## Model fitted: Michaelis-Menten (2 parms)
## 
## Parameter estimates:
## 
##               Estimate Std. Error  t-value p-value
## d:(Intercept) 534.1063    92.3057   5.7863  0.0007
## e:(Intercept)  18.3792    15.3117   1.2003  0.2690
## 
## Residual standard error:
## 
##  121.6792 (7 degrees of freedom)

tdla

The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.

## [1] "Substrates:"

## [1] "here are the rates"

## 
## Model fitted: Michaelis-Menten (2 parms)
## 
## Parameter estimates:
## 
##               Estimate Std. Error  t-value p-value
## d:(Intercept) 94.76996   22.19263  4.27033  0.0037
## e:(Intercept)  5.20492   10.73574  0.48482  0.6426
## 
## Residual standard error:
## 
##  36.04578 (7 degrees of freedom)

tdlb

The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.

## [1] "Substrates:"

## [1] "here are the rates"

## 
## Model fitted: Michaelis-Menten (2 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error   t-value p-value
## d:(Intercept) 580.84356  233.34931   2.48916  0.0416
## e:(Intercept)  65.06472   70.23579   0.92638  0.3851
## 
## Residual standard error:
## 
##  140.4322 (7 degrees of freedom)

fipronil

The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.

## [1] "Substrates:"

## [1] "here are the rates"

## 
## Model fitted: Michaelis-Menten (2 parms)
## 
## Parameter estimates:
## 
##                 Estimate Std. Error    t-value p-value
## d:(Intercept) 2.7431e+04 1.1366e+05 2.4133e-01  0.8212
## e:(Intercept) 2.1216e+03 9.6407e+03 2.2007e-01  0.8366
## 
## Residual standard error:
## 
##  973.0673 (4 degrees of freedom)

fipsulf

The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.

## [1] "Substrates:"

## [1] "here are the rates"

## 
## Model fitted: Michaelis-Menten (2 parms)
## 
## Parameter estimates:
## 
##               Estimate Std. Error  t-value p-value
## d:(Intercept) 102.0337     9.5991  10.6295  0.0000
## e:(Intercept)   2.2813     1.7809   1.2810  0.2475
## 
## Residual standard error:
## 
##  21.75614 (6 degrees of freedom)

exposure experiment analysis

Results presentation and discussion for parent analytes (atrazine, triadimenon, and fipronil) and their xenobiotic metabolites.

Toxicity of parents and metabolites discussion.

Microsomal analysis of soil and amphibian data for 0 (soil only), 2, 4, 12, 24, and 48 hours after exposure.

Database has factors for time, parent (mapped to analyte), analyte (can be either parent or metabolite), matrix (amphibian or soil), and tank (potentially a nuisance variable).

## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame':  352 obs. of  6 variables:
##  $ time     : int  2 2 2 2 4 4 4 4 12 12 ...
##  $ parent   : Factor w/ 3 levels "atrazine","fipronil",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ analyte  : Factor w/ 8 levels "atrazine","dea",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ matrix   : Factor w/ 2 levels "amphib","soil": 1 1 1 1 1 1 1 1 1 1 ...
##  $ conc     : num  3.07 3.69 4.7 1.89 7.62 ...
##  $ replicate: Factor w/ 4 levels "1","2","3","4": 1 2 3 4 1 2 3 4 1 2 ...
##  - attr(*, "vars")=List of 4
##   ..$ : symbol parent
##   ..$ : symbol analyte
##   ..$ : symbol matrix
##   ..$ : symbol time
##  - attr(*, "drop")= logi TRUE
##  - attr(*, "indices")=List of 86
##   ..$ : int  0 1 2 3
##   ..$ : int  4 5 6 7
##   ..$ : int  8 9 10 11
##   ..$ : int  12 13 14 15
##   ..$ : int  16 17 18 19
##   ..$ : int  20 21 22 23
##   ..$ : int  24 25 26 27
##   ..$ : int  28 29 30 31
##   ..$ : int  32 33 34 35
##   ..$ : int  36 37 38 39
##   ..$ : int  40 41 42 43
##   ..$ : int  88 89 90 91
##   ..$ : int  92 93 94 95
##   ..$ : int  96 97 98 99
##   ..$ : int  100 101 102 103
##   ..$ : int  104 105 106 107
##   ..$ : int  108 109 110 111
##   ..$ : int  112 113 114 115
##   ..$ : int  116 117 118 119
##   ..$ : int  120 121 122 123
##   ..$ : int  124 125 126 127
##   ..$ : int  128 129 130 131
##   ..$ : int  44 45 46 47
##   ..$ : int  48 49 50 51
##   ..$ : int  52 53 54 55
##   ..$ : int  56 57 58 59
##   ..$ : int  60 61 62 63
##   ..$ : int  64 65 66 67
##   ..$ : int  68 69 70 71
##   ..$ : int  72 73 74 75
##   ..$ : int  76 77 78 79
##   ..$ : int  80 81 82 83
##   ..$ : int  84 85 86 87
##   ..$ : int  264 265 266 267
##   ..$ : int  268 269 270 271
##   ..$ : int  272 273 274 275
##   ..$ : int  276 277 278 279
##   ..$ : int  280 281 282 283
##   ..$ : int  284 285 286 287 288 289 290 291
##   ..$ : int  292 293 294 295
##   ..$ : int  296 297 298 299
##   ..$ : int  300 301 302 303
##   ..$ : int  304 305 306 307
##   ..$ : int  308 309 310 311
##   ..$ : int  312 313 314 315
##   ..$ : int  316 317 318 319
##   ..$ : int  320 321 322 323
##   ..$ : int  324 325 326 327
##   ..$ : int  328 329 330 331 332 333 334 335
##   ..$ : int  336 337 338 339
##   ..$ : int  340 341 342 343
##   ..$ : int  344 345 346 347
##   ..$ : int  348 349 350 351
##   ..$ : int  176 177 178 179
##   ..$ : int  180 181 182 183
##   ..$ : int  184 185 186 187
##   ..$ : int  188 189 190 191
##   ..$ : int  192 193 194 195
##   ..$ : int  196 197 198 199
##   ..$ : int  200 201 202 203
##   ..$ : int  204 205 206 207
##   ..$ : int  208 209 210 211
##   ..$ : int  212 213 214 215
##   ..$ : int  216 217 218 219
##   ..$ : int  220 221 222 223
##   ..$ : int  224 225 226 227
##   ..$ : int  228 229 230 231
##   ..$ : int  232 233 234 235
##   ..$ : int  236 237 238 239
##   ..$ : int  240 241 242 243
##   ..$ : int  244 245 246 247
##   ..$ : int  248 249 250 251
##   ..$ : int  252 253 254 255
##   ..$ : int  256 257 258 259
##   ..$ : int  260 261 262 263
##   ..$ : int  132 133 134 135
##   ..$ : int  136 137 138 139
##   ..$ : int  140 141 142 143
##   ..$ : int  144 145 146 147
##   ..$ : int  148 149 150 151
##   ..$ : int  152 153 154 155
##   ..$ : int  156 157 158 159
##   ..$ : int  160 161 162 163
##   ..$ : int  164 165 166 167
##   ..$ : int  168 169 170 171
##   ..$ : int  172 173 174 175
##  - attr(*, "group_sizes")= int  4 4 4 4 4 4 4 4 4 4 ...
##  - attr(*, "biggest_group_size")= int 8
##  - attr(*, "labels")='data.frame':   86 obs. of  4 variables:
##   ..$ parent : Factor w/ 3 levels "atrazine","fipronil",..: 1 1 1 1 1 1 1 1 1 1 ...
##   ..$ analyte: Factor w/ 8 levels "atrazine","dea",..: 1 1 1 1 1 1 1 1 1 1 ...
##   ..$ matrix : Factor w/ 2 levels "amphib","soil": 1 1 1 1 1 2 2 2 2 2 ...
##   ..$ time   : int  2 4 12 24 48 0 2 4 12 24 ...
##   ..- attr(*, "vars")=List of 4
##   .. ..$ : symbol parent
##   .. ..$ : symbol analyte
##   .. ..$ : symbol matrix
##   .. ..$ : symbol time
##   ..- attr(*, "drop")= logi TRUE

## Source: local data frame [352 x 6]
## Groups: parent, analyte, matrix, time [86]
## 
##     time   parent  analyte matrix     conc replicate
##    (int)   (fctr)   (fctr) (fctr)    (dbl)    (fctr)
## 1      2 atrazine atrazine amphib 3.068203         1
## 2      2 atrazine atrazine amphib 3.689640         2
## 3      2 atrazine atrazine amphib 4.701166         3
## 4      2 atrazine atrazine amphib 1.892494         4
## 5      4 atrazine atrazine amphib 7.624610         1
## 6      4 atrazine atrazine amphib 4.134300         2
## 7      4 atrazine atrazine amphib 2.050779         3
## 8      4 atrazine atrazine amphib 7.626486         4
## 9     12 atrazine atrazine amphib 2.452278         1
## 10    12 atrazine atrazine amphib 3.786893         2
## ..   ...      ...      ...    ...      ...       ...

## Source: local data frame [86 x 7]
## Groups: parent, analyte, matrix [?]
## 
##      parent  analyte matrix  time count  ConcMean    ConcSD
##      (fctr)   (fctr) (fctr) (int) (int)     (dbl)     (dbl)
## 1  atrazine atrazine amphib     2     4  3.337876 1.1753225
## 2  atrazine atrazine amphib     4     4  5.359044 2.7518903
## 3  atrazine atrazine amphib    12     4  3.123811 1.2566289
## 4  atrazine atrazine amphib    24     4  1.554919 0.6096102
## 5  atrazine atrazine amphib    48     4  1.001887 0.3545473
## 6  atrazine atrazine   soil     0     4 15.930920 2.5911041
## 7  atrazine atrazine   soil     2     4 19.029034 4.3534303
## 8  atrazine atrazine   soil     4     4 18.770091 2.6953158
## 9  atrazine atrazine   soil    12     4 20.614190 3.8918511
## 10 atrazine atrazine   soil    24     4 24.996196 4.4473828
## ..      ...      ...    ...   ...   ...       ...       ...

## <!-- html table generated in R 3.2.3 by xtable 1.8-2 package -->
## <!-- Tue Mar 29 16:05:23 2016 -->
## <table border=1>
## <tr> <th>  </th> <th> parent </th> <th> analyte </th> <th> matrix </th> <th> time </th> <th> count </th> <th> ConcMean </th> <th> ConcSD </th>  </tr>
##   <tr> <td align="right"> 1 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 3.34 </td> <td align="right"> 1.18 </td> </tr>
##   <tr> <td align="right"> 2 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 5.36 </td> <td align="right"> 2.75 </td> </tr>
##   <tr> <td align="right"> 3 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 3.12 </td> <td align="right"> 1.26 </td> </tr>
##   <tr> <td align="right"> 4 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 1.55 </td> <td align="right"> 0.61 </td> </tr>
##   <tr> <td align="right"> 5 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 1.00 </td> <td align="right"> 0.35 </td> </tr>
##   <tr> <td align="right"> 6 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right">   0 </td> <td align="right">   4 </td> <td align="right"> 15.93 </td> <td align="right"> 2.59 </td> </tr>
##   <tr> <td align="right"> 7 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 19.03 </td> <td align="right"> 4.35 </td> </tr>
##   <tr> <td align="right"> 8 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 18.77 </td> <td align="right"> 2.70 </td> </tr>
##   <tr> <td align="right"> 9 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 20.61 </td> <td align="right"> 3.89 </td> </tr>
##   <tr> <td align="right"> 10 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 25.00 </td> <td align="right"> 4.45 </td> </tr>
##   <tr> <td align="right"> 11 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 26.43 </td> <td align="right"> 4.38 </td> </tr>
##   <tr> <td align="right"> 12 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 13 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.03 </td> </tr>
##   <tr> <td align="right"> 14 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.05 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 15 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 16 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.02 </td> </tr>
##   <tr> <td align="right"> 17 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right">   0 </td> <td align="right">   4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 18 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 19 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 20 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 21 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 22 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 23 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.33 </td> <td align="right"> 0.12 </td> </tr>
##   <tr> <td align="right"> 24 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.94 </td> <td align="right"> 0.76 </td> </tr>
##   <tr> <td align="right"> 25 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 1.08 </td> <td align="right"> 0.51 </td> </tr>
##   <tr> <td align="right"> 26 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 1.99 </td> <td align="right"> 1.35 </td> </tr>
##   <tr> <td align="right"> 27 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.66 </td> <td align="right"> 0.53 </td> </tr>
##   <tr> <td align="right"> 28 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right">   0 </td> <td align="right">   4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 29 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> -0.01 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 30 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.02 </td> </tr>
##   <tr> <td align="right"> 31 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.08 </td> <td align="right"> 0.07 </td> </tr>
##   <tr> <td align="right"> 32 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.03 </td> </tr>
##   <tr> <td align="right"> 33 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.06 </td> <td align="right"> 0.06 </td> </tr>
##   <tr> <td align="right"> 34 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 1.87 </td> <td align="right"> 1.35 </td> </tr>
##   <tr> <td align="right"> 35 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 1.30 </td> <td align="right"> 0.52 </td> </tr>
##   <tr> <td align="right"> 36 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.57 </td> <td align="right"> 0.24 </td> </tr>
##   <tr> <td align="right"> 37 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.27 </td> <td align="right"> 0.05 </td> </tr>
##   <tr> <td align="right"> 38 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.21 </td> <td align="right"> 0.13 </td> </tr>
##   <tr> <td align="right"> 39 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right">   2 </td> <td align="right">   8 </td> <td align="right"> 0.41 </td> <td align="right"> 0.45 </td> </tr>
##   <tr> <td align="right"> 40 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.87 </td> <td align="right"> 0.24 </td> </tr>
##   <tr> <td align="right"> 41 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.82 </td> <td align="right"> 0.17 </td> </tr>
##   <tr> <td align="right"> 42 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.70 </td> <td align="right"> 0.11 </td> </tr>
##   <tr> <td align="right"> 43 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.36 </td> <td align="right"> 0.06 </td> </tr>
##   <tr> <td align="right"> 44 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.35 </td> <td align="right"> 0.29 </td> </tr>
##   <tr> <td align="right"> 45 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.40 </td> <td align="right"> 0.48 </td> </tr>
##   <tr> <td align="right"> 46 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.61 </td> <td align="right"> 0.57 </td> </tr>
##   <tr> <td align="right"> 47 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 1.25 </td> <td align="right"> 0.53 </td> </tr>
##   <tr> <td align="right"> 48 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.91 </td> <td align="right"> 0.57 </td> </tr>
##   <tr> <td align="right"> 49 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right">   2 </td> <td align="right">   8 </td> <td align="right"> 0.01 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 50 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 51 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 52 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 53 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 54 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.06 </td> <td align="right"> 0.02 </td> </tr>
##   <tr> <td align="right"> 55 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.08 </td> <td align="right"> 0.05 </td> </tr>
##   <tr> <td align="right"> 56 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.02 </td> </tr>
##   <tr> <td align="right"> 57 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 58 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 59 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right">   0 </td> <td align="right">   4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 60 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 61 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 62 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 63 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 64 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 65 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.08 </td> <td align="right"> 0.02 </td> </tr>
##   <tr> <td align="right"> 66 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.13 </td> <td align="right"> 0.05 </td> </tr>
##   <tr> <td align="right"> 67 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.08 </td> <td align="right"> 0.05 </td> </tr>
##   <tr> <td align="right"> 68 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 69 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.03 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 70 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right">   0 </td> <td align="right">   4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 71 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 72 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 73 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.00 </td> </tr>
##   <tr> <td align="right"> 74 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 75 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.01 </td> </tr>
##   <tr> <td align="right"> 76 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 0.39 </td> <td align="right"> 0.08 </td> </tr>
##   <tr> <td align="right"> 77 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 0.55 </td> <td align="right"> 0.30 </td> </tr>
##   <tr> <td align="right"> 78 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 0.75 </td> <td align="right"> 0.32 </td> </tr>
##   <tr> <td align="right"> 79 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 0.87 </td> <td align="right"> 0.86 </td> </tr>
##   <tr> <td align="right"> 80 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 0.25 </td> <td align="right"> 0.08 </td> </tr>
##   <tr> <td align="right"> 81 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right">   0 </td> <td align="right">   4 </td> <td align="right"> 3.11 </td> <td align="right"> 0.85 </td> </tr>
##   <tr> <td align="right"> 82 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right">   2 </td> <td align="right">   4 </td> <td align="right"> 3.66 </td> <td align="right"> 1.34 </td> </tr>
##   <tr> <td align="right"> 83 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right">   4 </td> <td align="right">   4 </td> <td align="right"> 5.28 </td> <td align="right"> 0.37 </td> </tr>
##   <tr> <td align="right"> 84 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right">  12 </td> <td align="right">   4 </td> <td align="right"> 5.93 </td> <td align="right"> 1.00 </td> </tr>
##   <tr> <td align="right"> 85 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right">  24 </td> <td align="right">   4 </td> <td align="right"> 4.82 </td> <td align="right"> 0.32 </td> </tr>
##   <tr> <td align="right"> 86 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right">  48 </td> <td align="right">   4 </td> <td align="right"> 4.18 </td> <td align="right"> 0.44 </td> </tr>
##    </table>

The amphibian data set summary statistics. Atrazine peaks at 4 hours and then declines monotonically. DEA peaks at 12 hours and DIA at 24. Fipronil steadily declines from its first observation at 2 hours with fipronil sulfone not peaking until 24 hours. Triadimenon peaks at 24 hours with its metabolites tdla and tdlb peaking at 4 hours.

## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame':  86 obs. of  7 variables:
##  $ parent  : Factor w/ 3 levels "atrazine","fipronil",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ analyte : Factor w/ 8 levels "atrazine","dea",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ matrix  : Factor w/ 2 levels "amphib","soil": 1 1 1 1 1 2 2 2 2 2 ...
##  $ time    : int  2 4 12 24 48 0 2 4 12 24 ...
##  $ count   : int  4 4 4 4 4 4 4 4 4 4 ...
##  $ ConcMean: num  3.34 5.36 3.12 1.55 1 ...
##  $ ConcSD  : num  1.175 2.752 1.257 0.61 0.355 ...
##  - attr(*, "vars")=List of 3
##   ..$ : symbol parent
##   ..$ : symbol analyte
##   ..$ : symbol matrix
##  - attr(*, "drop")= logi TRUE

## Source: local data frame [6 x 7]
## Groups: parent, analyte, matrix [2]
## 
##     parent  analyte matrix  time count   ConcMean     ConcSD
##     (fctr)   (fctr) (fctr) (int) (int)      (dbl)      (dbl)
## 1 atrazine atrazine amphib     2     4 3.33787563 1.17532253
## 2 atrazine atrazine amphib     4     4 5.35904387 2.75189034
## 3 atrazine atrazine amphib    12     4 3.12381130 1.25662887
## 4 atrazine atrazine amphib    24     4 1.55491895 0.60961016
## 5 atrazine atrazine amphib    48     4 1.00188749 0.35454734
## 6 atrazine      dea amphib     2     4 0.01389877 0.00522407

## Source: local data frame [15 x 7]
## Groups: parent, analyte, matrix [3]
## 
##      parent  analyte matrix  time count   ConcMean     ConcSD
##      (fctr)   (fctr) (fctr) (int) (int)      (dbl)      (dbl)
## 1  atrazine atrazine amphib     2     4 3.33787563 1.17532253
## 2  atrazine atrazine amphib     4     4 5.35904387 2.75189034
## 3  atrazine atrazine amphib    12     4 3.12381130 1.25662887
## 4  atrazine atrazine amphib    24     4 1.55491895 0.60961016
## 5  atrazine atrazine amphib    48     4 1.00188749 0.35454734
## 6  atrazine      dea amphib     2     4 0.01389877 0.00522407
## 7  atrazine      dea amphib     4     4 0.04436711 0.02843181
## 8  atrazine      dea amphib    12     4 0.05402182 0.01282235
## 9  atrazine      dea amphib    24     4 0.03652473 0.01310278
## 10 atrazine      dea amphib    48     4 0.01951501 0.01958540
## 11 atrazine      dia amphib     2     4 0.32878185 0.12495746
## 12 atrazine      dia amphib     4     4 0.94192818 0.76074427
## 13 atrazine      dia amphib    12     4 1.08350960 0.50576827
## 14 atrazine      dia amphib    24     4 1.98835970 1.34925248
## 15 atrazine      dia amphib    48     4 0.66238821 0.53051448

## [1] "atrazine"
## [1] "dea"
## [1] "dia"

## Source: local data frame [10 x 7]
## Groups: parent, analyte, matrix [2]
## 
##      parent  analyte matrix  time count  ConcMean     ConcSD
##      (fctr)   (fctr) (fctr) (int) (int)     (dbl)      (dbl)
## 1  fipronil fipronil amphib     2     4 1.8670398 1.34631230
## 2  fipronil fipronil amphib     4     4 1.3005836 0.52188454
## 3  fipronil fipronil amphib    12     4 0.5659565 0.24026633
## 4  fipronil fipronil amphib    24     4 0.2676732 0.05106111
## 5  fipronil fipronil amphib    48     4 0.2119504 0.13140536
## 6  fipronil  fipsulf amphib     2     4 0.3465734 0.28711156
## 7  fipronil  fipsulf amphib     4     4 0.3966089 0.47739157
## 8  fipronil  fipsulf amphib    12     4 0.6129114 0.57346517
## 9  fipronil  fipsulf amphib    24     4 1.2528179 0.52648677
## 10 fipronil  fipsulf amphib    48     4 0.9067126 0.56596417

## Source: local data frame [0 x 7]
## Groups: parent, analyte, matrix [0]
## 
## Variables not shown: parent (fctr), analyte (fctr), matrix (fctr), time
##   (int), count (int), ConcMean (dbl), ConcSD (dbl)

The soil data set summary statistics. Atrazine in soil showing an upwards trend (need to test if significant) with dia and dea levels very low indicating little degradation in soil over the 48 hour period. Fipronil and triadimenon data also indicating little degradation in soil.

## Source: local data frame [18 x 7]
## Groups: parent, analyte, matrix [3]
## 
##      parent  analyte matrix  time count     ConcMean      ConcSD
##      (fctr)   (fctr) (fctr) (int) (int)        (dbl)       (dbl)
## 1  atrazine atrazine   soil     0     4 15.930919692 2.591104068
## 2  atrazine atrazine   soil     2     4 19.029034412 4.353430337
## 3  atrazine atrazine   soil     4     4 18.770091080 2.695315758
## 4  atrazine atrazine   soil    12     4 20.614190363 3.891851074
## 5  atrazine atrazine   soil    24     4 24.996195532 4.447382801
## 6  atrazine atrazine   soil    48     4 26.434744900 4.377099498
## 7  atrazine      dea   soil     0     4  0.000000000 0.000000000
## 8  atrazine      dea   soil     2     4  0.001570429 0.000633009
## 9  atrazine      dea   soil     4     4  0.002999216 0.001537652
## 10 atrazine      dea   soil    12     4  0.005469015 0.004234880
## 11 atrazine      dea   soil    24     4  0.007308439 0.001736211
## 12 atrazine      dea   soil    48     4  0.008784632 0.003772339
## 13 atrazine      dia   soil     0     4  0.000000000 0.000000000
## 14 atrazine      dia   soil     2     4 -0.007727286 0.007374085
## 15 atrazine      dia   soil     4     4  0.020538600 0.020166308
## 16 atrazine      dia   soil    12     4  0.077036921 0.067772652
## 17 atrazine      dia   soil    24     4  0.042975804 0.030007793
## 18 atrazine      dia   soil    48     4  0.063693292 0.058241606

## Source: local data frame [10 x 7]
## Groups: parent, analyte, matrix [2]
## 
##      parent  analyte matrix  time count   ConcMean      ConcSD
##      (fctr)   (fctr) (fctr) (int) (int)      (dbl)       (dbl)
## 1  fipronil fipronil   soil     2     8 0.40768123 0.447265792
## 2  fipronil fipronil   soil     4     4 0.86826316 0.244191727
## 3  fipronil fipronil   soil    12     4 0.81738069 0.169691335
## 4  fipronil fipronil   soil    24     4 0.70225877 0.108745058
## 5  fipronil fipronil   soil    48     4 0.35560948 0.058543752
## 6  fipronil  fipsulf   soil     2     8 0.01022479 0.012898421
## 7  fipronil  fipsulf   soil     4     4 0.02193831 0.010765665
## 8  fipronil  fipsulf   soil    12     4 0.01829893 0.005013814
## 9  fipronil  fipsulf   soil    24     4 0.01917848 0.009139211
## 10 fipronil  fipsulf   soil    48     4 0.01262887 0.005517408

## Source: local data frame [0 x 7]
## Groups: parent, analyte, matrix [0]
## 
## Variables not shown: parent (fctr), analyte (fctr), matrix (fctr), time
##   (int), count (int), ConcMean (dbl), ConcSD (dbl)

pdf(paste(micro.graphics,"data_mean_scatterplot",".pdf", sep=""))
  par(mfrow=c(3,1))
  print(parents)

## [1] atrazine    triadimefon fipronil   
## Levels: atrazine fipronil triadimefon

  for(parent in parents){
    i=0
    print(parent)
    temp.parent <- micro.group.stats.amphib[which(micro.group.stats.amphib$parent==parent),]
    print(temp.parent)
    parent.analytes <- unique(temp.parent$analyte)
    for(analyte in parent.analytes){
      print(parent)
      print(analyte) 
      analytetemp <- micro.group.stats.amphib[which(micro.group.stats.amphib$analyte==analyte),]
      xvalues <- as.numeric(as.character(analytetemp$time))
      points.y <- micro.amphib[which(micro.amphib$analyte==analyte),]$conc
      points.x <- as.numeric(as.character(micro.amphib[which(micro.amphib$analyte==analyte),]$time))
      #create empty plot if needed
      if(i==0){
        parenttemp <- micro.group.stats.amphib[which(micro.group.stats.amphib$parent==parent),]
        maxconc <- max(points.y) 
        plot(xvalues,analytetemp$ConcMean,type="l", xlim=c(0,48),
             ylim = c(0,maxconc), main=parent,col="black", xlab="Hours", ylab="Concentration")
        axis(1,at=xvalues)
        i=1
      }
      #add line for parent metabolite
      if(analyte %in% parents){
        lines(xvalues,analytetemp$ConcMean,type="l",col="red")
        points(points.x, points.y, col = "red")
      }else{
      #add line for daughter metabolite
        lines(xvalues,analytetemp$ConcMean,type="l",col="blue")
        points(points.x, points.y, col = "blue")
      }
    }
  }

## [1] "atrazine"
## Source: local data frame [15 x 7]
## Groups: parent, analyte, matrix [3]
## 
##      parent  analyte matrix  time count   ConcMean     ConcSD
##      (fctr)   (fctr) (fctr) (int) (int)      (dbl)      (dbl)
## 1  atrazine atrazine amphib     2     4 3.33787563 1.17532253
## 2  atrazine atrazine amphib     4     4 5.35904387 2.75189034
## 3  atrazine atrazine amphib    12     4 3.12381130 1.25662887
## 4  atrazine atrazine amphib    24     4 1.55491895 0.60961016
## 5  atrazine atrazine amphib    48     4 1.00188749 0.35454734
## 6  atrazine      dea amphib     2     4 0.01389877 0.00522407
## 7  atrazine      dea amphib     4     4 0.04436711 0.02843181
## 8  atrazine      dea amphib    12     4 0.05402182 0.01282235
## 9  atrazine      dea amphib    24     4 0.03652473 0.01310278
## 10 atrazine      dea amphib    48     4 0.01951501 0.01958540
## 11 atrazine      dia amphib     2     4 0.32878185 0.12495746
## 12 atrazine      dia amphib     4     4 0.94192818 0.76074427
## 13 atrazine      dia amphib    12     4 1.08350960 0.50576827
## 14 atrazine      dia amphib    24     4 1.98835970 1.34925248
## 15 atrazine      dia amphib    48     4 0.66238821 0.53051448
## [1] "atrazine"
## [1] "atrazine"

## [1] "atrazine"
## [1] "dea"
## [1] "atrazine"
## [1] "dia"
## [1] "triadimefon"
## Source: local data frame [15 x 7]
## Groups: parent, analyte, matrix [3]
## 
##         parent     analyte matrix  time count   ConcMean      ConcSD
##         (fctr)      (fctr) (fctr) (int) (int)      (dbl)       (dbl)
## 1  triadimefon        tdla amphib     2     4 0.05514470 0.021089764
## 2  triadimefon        tdla amphib     4     4 0.08150175 0.046221502
## 3  triadimefon        tdla amphib    12     4 0.03632384 0.015222899
## 4  triadimefon        tdla amphib    24     4 0.02400790 0.007522554
## 5  triadimefon        tdla amphib    48     4 0.01549908 0.004845715
## 6  triadimefon        tdlb amphib     2     4 0.08362714 0.016364595
## 7  triadimefon        tdlb amphib     4     4 0.13009543 0.054315693
## 8  triadimefon        tdlb amphib    12     4 0.07664673 0.048553355
## 9  triadimefon        tdlb amphib    24     4 0.04183235 0.011652266
## 10 triadimefon        tdlb amphib    48     4 0.02950214 0.003438978
## 11 triadimefon triadimefon amphib     2     4 0.38678831 0.075852995
## 12 triadimefon triadimefon amphib     4     4 0.54862458 0.300394386
## 13 triadimefon triadimefon amphib    12     4 0.75491439 0.318237965
## 14 triadimefon triadimefon amphib    24     4 0.86540039 0.860421208
## 15 triadimefon triadimefon amphib    48     4 0.24868748 0.079848623
## [1] "triadimefon"
## [1] "tdla"

## [1] "triadimefon"
## [1] "tdlb"
## [1] "triadimefon"
## [1] "triadimefon"
## [1] "fipronil"
## Source: local data frame [10 x 7]
## Groups: parent, analyte, matrix [2]
## 
##      parent  analyte matrix  time count  ConcMean     ConcSD
##      (fctr)   (fctr) (fctr) (int) (int)     (dbl)      (dbl)
## 1  fipronil fipronil amphib     2     4 1.8670398 1.34631230
## 2  fipronil fipronil amphib     4     4 1.3005836 0.52188454
## 3  fipronil fipronil amphib    12     4 0.5659565 0.24026633
## 4  fipronil fipronil amphib    24     4 0.2676732 0.05106111
## 5  fipronil fipronil amphib    48     4 0.2119504 0.13140536
## 6  fipronil  fipsulf amphib     2     4 0.3465734 0.28711156
## 7  fipronil  fipsulf amphib     4     4 0.3966089 0.47739157
## 8  fipronil  fipsulf amphib    12     4 0.6129114 0.57346517
## 9  fipronil  fipsulf amphib    24     4 1.2528179 0.52648677
## 10 fipronil  fipsulf amphib    48     4 0.9067126 0.56596417
## [1] "fipronil"
## [1] "fipronil"

## [1] "fipronil"
## [1] "fipsulf"

dev.off()

## quartz_off_screen 
##                 2

la de da

turn times series to zoo objects

estimate lambdas using maximum likelihood for each time series

library(maxLik)

MLexp <- function(times, data){

expLik <- function(param) { y <- data t <- times alpha1 <- param[1] lambda1 <- param[2]-1(alpha1-lambda1t) - y/(exp(alpha1-lambda1*t)) }

max.fit <- maxLik(expLik, start = c(1,1)) crit <- qt(.975, length(data)-2) max.intercept.CI <- c(summary(max.fit)\(est[1,1]-crit*summary(max.fit)\)est[1,2],summary(max.fit)\(est[1,1]+crit*summary(max.fit)\)est[1,2]) max.decayrate.CI <- c(-1summary(max.fit)\(est[2,1]-crit*summary(max.fit)\)est[2,2],-1summary(max.fit)\(est[2,1]+crit*summary(max.fit)\)est[2,2]) max.halflife.CI <- c(log(2)/max.decayrate.CI[2],log(2)/max.decayrate.CI[1])

paste(“MLE for Initial Concentration =”,max.fit\(est[1], "MLE for decay rate =", max.fit\)est[2], “MLE for half life =”, -log(.5)/max.fit$est[2])

temp.list <- list(max.fit\(est[1], max.fit\)est[2], -log(.5)/max.fit$est[2], max.intercept.CI, max.decayrate.CI, max.halflife.CI, vcov(max.fit) ) return(temp.list) }

MLexp.intercept <- function(times, data) { temp.fit <- MLexp(times,data) temp.fit[[1]] }

MLexp.decayrate <- function(times, data) { temp.fit <- MLexp(times,data) temp.fit[[2]] }

MLexp.halflife <- function(times,data) { temp.fit <- MLexp(times,data) temp.fit[[3]] }

MLexp.intercept.CI <- function(times,data){ temp.fit <- MLexp(times,data) temp2 <- temp.fit[[4]] names(temp2) <- c(“Lower95”, “Upper95”) temp2 }

MLexp.decayrate.CI <- function(times,data){ temp.fit <- MLexp(times,data) temp2 <- temp.fit[[5]] names(temp2) <- c(“Lower95”, “Upper95”) temp2 }

MLexp.halflife.CI <- function(times,data){ temp.fit <- MLexp(times,data) temp2 <- temp.fit[[6]] names(temp2) <- c(“Lower95”, “Upper95”) temp2 }

microsome experiment analysis

data in csv format